Abstract: There are provided systems and methods for predictive data aggregations for real-time detection of anomalous data. A service provider, such as an electronic transaction processor for digital transactions, may access feature data for accounts prior to the feature data being used in a live risk analysis system, for example, at a designated time and/or for a designated time period. The service provider may predetermine data values from the feature data, such as aggregates of the feature data that are for certain time periods and utilized by the live risk analysis system. This processing may be done in a batch processing job in order to determine data values for multiple accounts. These data values are prestored in an available database for a distributed computing system of the service provider. Thereafter, when the live risk analysis system requires the data values, the data values may be immediately retrieved.

Abstract: Techniques are disclosed relating to detecting malicious transactions using multi-level risk analysis. In some embodiments, a server system may receive a transaction request for a transaction associated with a transaction system. In some embodiments, the server system may perform an initial risk-assessment to generate a risk score for the transaction, where the initial risk-assessment is an assessment procedure applied for transactions associated with a various transaction systems. In some embodiments, the server system may determine, based on the risk score, that the transaction does not pass the initial risk-assessment. The server system may determine, based on account information associated with the transaction system, whether a risk-assessment override option is enabled. In various embodiments, in response to a determination that the risk-assessment override option is enabled, the server system may perform additional fraud-detection operations to determine whether to authorize the transaction.